Operational S2S Strategy: From Training to Live Forecasting

The Challenge

You plan to train your model using ERA5 (Reanalysis) as inputs (XX) and CHIRPS as targets (YY).

To make this operational, you need Real-Time Inputs that "look like" ERA5 but are available today.


Option 1: The "Direct S2S" Approach (Pure AI Forecast)

Your model acts as the simulator. It takes Today's State \to Predicts Week 3-4.

1. Atmospheric Inputs (Wind, Geopotential, Humidity)

Since ERA5 is produced by ECMWF, the best operational proxy is the ECMWF Operational Analysis (IFS).

2. Ocean Inputs (SST)

The ocean changes slowly, so having the absolute latest hour is less critical, but you still need daily updates.


Option 2: The "Hybrid" Approach (Model Output Statistics / Downscaling)

Your model corrects a Physical Model. Input is a coarse Physical Forecast \to Predicts High-Res Reality.

In this scenario, you do not train on ERA5. Instead, you train on Reforecasts (Hindcasts) from a dynamical model.

Verdict: Since you specifically asked about Training on ERA5, you are likely following Option 1 (Direct S2S).


Variable Training Data (Historical) Operational Data (Live) Notes
Atmosphere (Wind, Z500, etc.) ERA5 Reanalysis ERA5T (Preliminary) or GFS Analysis ERA5T has ~5 day lag. If you need immediate runs, use GFS Analysis but verify "Domain Shift".
SST (ENSO, IOD) ERA5 SST NOAA OISST v2 High consistency. Regrid OISST to match your ERA5 grid.
Precipitation (Ground Truth) CHIRPS v2.0 CHIRPS-Prelim CHIRPS releases a preliminary version rapidly for monitoring.

Handling the "Lag" (The 5-day Gap)

If you use ERA5T (5-day delay) for operations:

Summary Checklist for Operations

  1. Download ERA5T: Set up a cron job to fetch the latest preliminary ERA5 data (last month).
  2. Download OISST: Fetch the daily SST update.
  3. Preprocessing: Apply the exact same normalization (Mean/Std) you saved during training. Do not re-calculate statistics on the live data.
  4. Inference: Run the U-Net/cGAN.